Learning AI if You Suck at Math

Maybe you’d love to dig deeper and get an image recognition program running in TensorFlow or Theano? Perhaps you’re a kick-ass developer or systems architect and you know computers incredibly well but there’s just one little problem:

You suck at math.

That’s all right! I share your dirty little secret and I have some books and websites that will really help you get rolling fast.

Turns out, we have a few things to figure out before General AI can pop from the primordial digital goo. Mostly nothing ever came of the early AI promises in the 80’s and 90’s. Hype never lived up to reality and AI winter followed AI winter.

All that changed in the last few years with the sudden success of Deep Learning.

Maybe you saw the story in the New York Times that showed how Googletransformed its translation services almost overnight delivering accuracy that rivaled professional translators? In a mere nine months they outpaced what the platform managed in the seven previous years combined.

No matter how you cut it, AI solves big, intractable problems that have eluded us for decades.We know how to drive a car yet we can’t tell machines how to do it. But we can let machines figure it out for themselves.For once a technology coming out of Silicon Valley is not just hype. It’s real. AI is hot for good reasons.

The problem is you have to understand a nested layer of logic, terms, symbols and ideas that are all interrelated but you have no foundation for any of it. So it basically sounds like an alien language. You probably understand all the text leading up to it but the rest is just gibberish. It can turn really disheartening fast.

But have no fear! All is not lost.

I’m here to help you wade through the swamp with some books that will get you crunching numbers like a savant. All right, well maybe you won’t be Daniel Tammet, but you can put aside those painful memories of times-table memorization and get cranking. You CAN learn math as an adult.

I’ve tried to read a number of AI texts and tutorials. I understand the concepts intuitively.They make perfect sense to me.It’s just that when I see a string of symbols my brain glazes over and I have no idea what I’m reading. As a systems architect for much of my life I didn’t need much math. There are IP subnet cheat sheets and as long as I knew how far electricity could reasonably travel along the length of cables I could cut them andcrimp them appropriately. For most of my life, I needed to understand how systems get setup, how they work together and how they stay running.Systems administration is very boolean. It either works or it doesn’t. But AI and math works on a different side of the brain.

What I needed were some primers written for adults that treat you like one. I also want books that answer questions about whymath works. In school, your reason for learning was probably “shut up and do it or else.” But as an adult you need more. You want to know how things work too.

This book breaks down the “why” of math beautifully. It relates the subject to the real world, gets into the philosophy and then quickly leaves thephilosophy behind because you don’t really need to understand whether fractions actually exist in nature or at the Platonic level. Instead, Timothy helped me understand that math is an abstraction layer. It breaks problems down into simpler, cleaner steps.The complexity of working out a model that precisely simulates particle interaction in a box would need to take into account an insane amount of real world physics properties like magnetic interference, gravity, the force of the collision, the particles initial direction and speed as well as much, much more. It turns out life is a complex series of algorithms. But here’s the trick. In practice you don’t need a perfect model. Instead, math looks to break the problem down into its essentialcomponents. What are the critical factors? Math gives you a general abstraction of the problem that can work with other problem sets as well. In essence, the numbers themselves don’t matter all that much. They are just variables. Math boils down to variables and rules. You can learn those variables and rules!

Now there are two other strong contenders for getting your math foundation built. The first is Mathematics for the Nonmathemetician by Morris Kline. The second is the No Bullshit Guide to Math and Physics by Ivan Savov.Each appeals to a different mindset. I prefer the very short intro because it gets down to brass tacks quickly and still manages to stay very relate-able. The “Math for Nonmathematicians” book is much longer and goes into more detail about the history of math and how it works in the world. Some people will really enjoy that approach. The “No Bullshit Guide” is quick and gets right into the equations fast with no answer as to why anything works the way it does. It’s basically a primer on the rules. That will appeal to folks who have less of a philosophical bent.

The next book you will want is Algebra Unplugged by Jim Loats and Kenn Amdahl. Now there are a few typos in the book but I don’t find them all that distracting. I appreciate the book’s approach to gentle learning that quickly accelerates. Some people get worked into a lather over typos but you’ve got bigger challenges to worry about. You’re trying to learn math when your brain isn’t quite as plastic as it once was in your youth! So let the typos go and see the big picture here. This book will really help you get moving in the right direction.

After you’ve got the math down you’ll want to dig intoMake Your Own Neural Network by Tariq Rashid. It also has some typos, but there is a great Github repo with updates that fixes most of them. This book is incredibly gentle and intuitive. It seems to anticipate your objections and fears the second you have them! The author is uncanny in how he spots your resistance as it happens. The book walks through only the math you need for neural networks specifically. It then works through Python, assuming you know nothing aboutprogramming. Finally it gets you coding your own neural network from scratch. Now while there are certainly tools out there that will work better for professional programs, it helps to try your hand at your own first, so you understand the basics.

Learning AI if You Suck at Math — Part 3 — Building an AI Dream Machine — This article guides you through getting a powerful deep learning machine setup and installed with all the latest and greatest frameworks.